Abstract:

Disclosed is an adaptive sound source vector quantization device capable
of improving quantization accuracy of adaptive sound source vector
quantization while suppressing increase of the calculation amount in CELP
sound encoding which performs encoding in sub-frame unit. In the device,
a search adaptive sound source vector generation unit (103) cuts out an
adaptive sound source vector of a frame length (n) from an adaptive sound
source codebook (102), a search impulse response matrix generation unit
(105) generates a search impulse response matrix of n n by using an
impulse response matrix for each of sub-frames inputted from a synthesis
filter (104), a search target vector generation unit (106) adds the
target vector of each sub-frame so as to generate a search target vector
of frame length (n), an evaluation scale calculation unit (107);
calculates the evaluation scale of the adaptive sound source vector
quantization by using the search adaptive sound source vector, the search
impulse response matrix, and the search target vector.

Claims:

1. An adaptive excitation vector quantization apparatus that is used in
code excited linear prediction speech encoding to generate linear
prediction residual vectors of a length m and linear prediction
coefficients by dividing a frame of a length n into a plurality of
subframes of the length m and performing a linear prediction analysis
(where n and m are integers, and n is an integral multiple of m), the
apparatus comprising:an adaptive excitation vector generating section
that cuts out an adaptive excitation vector of the length n from an
adaptive excitation codebook;a target vector forming section that forms a
target vector of the length n by adding the linear prediction residual
vectors of the plurality of subframes;a synthesis filter that generates
m×m impulse response matrixes using the linear prediction
coefficients of the plurality of subframes;an impulse response matrix
forming section that forms a n×n impulse response matrix using the
m×m impulse response matrixes;an evaluation measure calculating
section that calculates an evaluation measure of adaptive excitation
vector quantization per pitch period candidate, using the adaptive
excitation vector of the length n, the target vector of the length n and
the n×n impulse response matrix; andan evaluation measure
comparison section that compares the evaluation measures with respect to
the pitch period candidates and calculates a pitch period of a highest
evaluation measure as a quantization result.

3. An adaptive excitation vector dequantization apparatus that is used in
code excited linear prediction speech decoding to decode encoded
information acquired by dividing a frame into a plurality of subframes
and performing a linear prediction analysis in code excited linear
prediction encoding, the apparatus comprising:a storage section that
stores a pitch period acquired by performing adaptive excitation vector
quantization of the frame in the code excited linear prediction speech
encoding; andan adaptive excitation vector generating section that uses
the pitch period as a cutting point and cuts out an adaptive excitation
vector of a subframe length m from an adaptive excitation codebook.

5. An adaptive excitation vector quantization method that is used in code
excited linear prediction speech encoding to generate linear prediction
residual vectors of a length m and linear prediction coefficients by
dividing a frame of a length n into a plurality of subframes of the
length m and performing a linear prediction analysis (where n and m are
integers, and n is an integral multiple of m), the method comprising the
steps of:cutting out an adaptive excitation vector of the length n from
an adaptive excitation codebook;forming a target vector of the length n
by adding the linear prediction residual vectors of the plurality of
subframes;generating m×m impulse response matrixes using the linear
prediction coefficients of the plurality of subframes;forming a n×n
impulse response matrix using the m×m impulse response
matrixes;calculating an evaluation measure of adaptive excitation vector
quantization per pitch period candidate, using the adaptive excitation
vector of the length n, the target vector of the length n and the
n×n impulse response matrix; andcomparing the evaluation measures
with respect to the pitch period candidates and calculating a pitch
period of a highest evaluation measure as a quantization result.

Description:

TECHNICAL FIELD

[0001]The present invention relates to an adaptive excitation vector
quantization apparatus, adaptive excitation vector dequantization
apparatus and quantization and dequantization methods for vector
quantization of adaptive excitations in CELP (Code Excited Linear
Prediction) speech coding. In particular, the present invention relates
to an adaptive excitation vector quantization apparatus, adaptive
excitation vector dequantization apparatus and quantization and
dequantization methods for vector quantization of adaptive excitations
used in a speech encoding and decoding apparatus that transmits speech
signals, in fields such as a packet communication system represented by
Internet communication and a mobile communication system.

BACKGROUND

[0002]In the field of digital radio communication, packet communication
represented by Internet communication, speech storage and so on, speech
signal encoding and decoding techniques are essential for effective use
of channel capacity and storage media for radio waves. In particular, a
CELP speech encoding and decoding technique is a mainstream technique
(for example, see non-patent document 1).

[0003]A CELP speech encoding apparatus encodes input speech based on
speech models stored in advance. To be more specific, the CELP speech
encoding apparatus divides a digital speech signal into frames of regular
time intervals, for example, frames of approximately 10 to 20 ms,
performs a linear prediction analysis of a speech signal on a per frame
basis to find the linear prediction coefficients ("LPC's") and linear
prediction residual vector, and encodes the linear prediction
coefficients and linear prediction residual vector individually. A CELP
speech encoding or decoding apparatus encodes or decodes a linear
prediction residual vector using an adaptive excitation codebook storing
excitation signals generated in the past and a fixed codebook storing a
specific number of fixed-shape vectors (i.e. fixed code vectors). Here,
while the adaptive excitation codebook is used to represent the periodic
components of a linear prediction residual vector, the fixed codebook is
used to represent the non-periodic components of the linear prediction
residual vector that cannot be represented by the adaptive excitation
codebook.

[0004]Further, encoding or decoding processing of a linear prediction
residual vector is generally performed in units of subframes dividing a
frame into shorter time units (approximately 5 ms to 10 ms). In ITU-T
Recommendation G.729 disclosed in Non-Patent Document 2, an adaptive
excitation is vector-quantized by dividing a frame into two subframes and
by searching for the pitch periods of these subframes using an adaptive
excitation codebook. Such a method of adaptive excitation vector
quantization in subframe units makes it possible to reduce the amount of
calculations compared to the method of adaptive excitation vector
quantization in frame units. [0005]Non-Patent Document 1: M. R.
Schroeder, B. S. Atal "IEEE proc. ICASSP" 1985, "Code Excited Linear
Prediction: High Quality Speech at Low Bit Rate.right brkt-bot., pages
937-940 [0006]Non-Patent Document 2: "ITU-T Recommendation G.729," ITU-T,
1996/3, pages 17-19

DISCLOSURE OF INVENTION

Problem to be Solved by the Invention

[0007]However, regarding the amount of information involved in the pitch
period search processing in subframe units, in an apparatus that performs
the above-noted adaptive excitation vector quantization in subframe
units, for example, when one frame is divided into two subframes, the
amount of information involved in adaptive excitation vector quantization
per subframe is half the overall amount of information. Consequently,
when the overall amount of information involved in adaptive excitation
vector quantization is reduced, there is a problem that the amount of
information to use for each subframe is further reduced, the range of
pitch period search per subframe is limited, and the accuracy of adaptive
excitation vector quantization degrades. For example, when the amount of
information that is assigned to an adaptive excitation codebook is 8
bits, there are 256 patterns of pitch period candidates to search for.
However, when this information amount of 8 bits is equally distributed to
two subframes, a pitch period search is performed using 4 bits of
information in one subframe. Consequently, there are 16 patterns of pitch
period candidates to search for in each subframe, and variations to
express pitch periods are insufficient. On the other hand, if a CELP
speech encoding apparatus limits frame-unit processing to adaptive
excitation vector quantization processing and performs other processing
than adaptive excitation vector quantization in subframe units, it is
possible to suppress an increased of the amount of calculations due to
the adaptive excitation vector quantization, within an acceptable level.

[0008]It is therefore an object of the present invention to provide an
adaptive excitation vector quantization apparatus, adaptive excitation
vector dequantization apparatus, and quantization and dequantization
methods that can suppress an increase of the amount of calculations,
expand the range of pitch period search and improve the accuracy of
quantization of adaptive excitation vector quantization, in CELP speech
coding for performing linear prediction coding in subframe units.

Means for Solving the Problem

[0009]The adaptive excitation vector quantization apparatus of the present
invention that is used in code excited linear prediction speech encoding
to generate linear prediction residual vectors of a length m and linear
prediction coefficients by dividing a frame of a length n into a
plurality of subframes of the length m and performing a linear prediction
analysis (where n and m are integers, and n is an integral multiple of
m), employs a configuration having: an adaptive excitation vector
generating section that cuts out an adaptive excitation vector of the
length n from an adaptive excitation codebook; a target vector forming
section that forms a target vector of the length n by adding the linear
prediction residual vectors of the plurality of subframes; a synthesis
filter that generates m×m impulse response matrixes using the
linear prediction coefficients of the plurality of subframes; an impulse
response matrix forming section that forms a n×n impulse response
matrix using the m×m impulse response matrixes; an evaluation
measure calculating section that calculates an evaluation measure of
adaptive excitation vector quantization per pitch period candidate, using
the adaptive excitation vector of the length n, the target vector of the
length n and the n×n impulse response matrix; and an evaluation
measure comparison section that compares the evaluation measures with
respect to the pitch period candidates and calculates a pitch period of a
highest evaluation measure as a quantization result.

[0010]The adaptive excitation vector dequantization apparatus of the
present invention that is used in code excited linear prediction speech
decoding to decode encoded information acquired by dividing a frame into
a plurality of subframes and performing a linear prediction analysis in
code excited linear prediction decoding, employs a configuration having:
a storage section that stores a pitch period acquired by performing
adaptive excitation vector quantization of the frame in the code excited
linear prediction speech coding; and an adaptive excitation vector
generating section that uses the pitch period as a cutting point and cuts
out an adaptive excitation vector of a subframe length m from an adaptive
excitation codebook.

[0011]The adaptive excitation vector quantization method of the present
invention that is used in code excited linear prediction speech encoding
to generate linear prediction residual vectors of a length m and linear
prediction coefficients by dividing a frame of a length n into a
plurality of subframes of the length m and performing a linear prediction
analysis (where n and m are integers, and n is an integral multiple of
m), employs a configuration having the steps of: cutting out an adaptive
excitation vector of the length n from an adaptive excitation codebook;
forming a target vector of the length n by adding the linear prediction
residual vectors of the plurality of subframes; generating m×m
impulse response matrixes using the linear prediction coefficients of the
plurality of subframes; forming a n×n impulse response matrix using
the m×m impulse response matrixes; calculating an evaluation
measure of adaptive excitation vector quantization per pitch period
candidate, using the adaptive excitation vector of the length n, the
target vector of the length n and the n×n impulse response matrix;
and comparing the evaluation measures with respect to the pitch period
candidates and calculating a pitch period of a highest evaluation measure
as a quantization result.

Advantageous Effect of the Invention

[0012]According to the present invention, by using linear prediction
coefficients and linear prediction residual vectors that are generated in
subframe units in CELP speech encoding that performs linear prediction
encoding in subframe units, forming a target vector, an adaptive
excitation vector and an impulse response matrix in frame units, and
performing adaptive excitation vector quantization in frame units, it is
possible to suppress an increase of the amount of calculations, expand
the range of pitch period search, improve the accuracy of adaptive
excitation vector quantization and, furthermore, improve the quality of
CELP speech coding.

BRIEF DESCRIPTION OF DRAWINGS

[0013]FIG. 1 is a block diagram showing main components of an adaptive
excitation vector quantization apparatus according to an embodiment of
the present invention;

[0014]FIG. 2 illustrates an excitation produced in an adaptive excitation
codebook according to an embodiment of the present invention; and

[0015]FIG. 3 is a block diagram showing main components of an adaptive
excitation vector dequantization apparatus according to an embodiment of
the present invention.

BEST MODE FOR CARRYING OUT THE INVENTION

[0016]An example case will be described with an embodiment of the present
invention, where a CELP speech encoding apparatus including an adaptive
excitation vector quantization apparatus divides each frame forming a
speech signal of 16 kHz into two subframes, performs a linear prediction
analysis of each subframe, and calculates a linear prediction coefficient
and linear prediction residual vector per subframe. Unlike a conventional
adaptive excitation vector quantization apparatus that performs a pitch
period search per subframe to quantize an adaptive excitation vector, the
adaptive excitation vector quantization apparatus according to the
present embodiment groups two subframes into one frame and performs a
pitch period search using 8 bits of information.

[0017]An embodiment of the present invention will be explained below in
detail with reference to the accompanying drawings.

Embodiment

[0018]FIG. 1 is a block diagram showing main components of adaptive
excitation vector quantization apparatus according to an embodiment of
the present invention.

[0020]Pitch period designation section 101 sequentially designates pitch
periods in a predetermined range of pitch period search, to search
adaptive excitation vector generating section 103, based on subframe
indices that are received as input on a per subframe basis.

[0021]Adaptive excitation codebook 102 has a built-in buffer storing
excitations, and updates the excitations using a pitch period index IDX
fed back from evaluation measure comparison section 108 every time a
pitch period search is finished on a per frame basis.

[0022]Search adaptive excitation vector generating section 103 cuts out,
from adaptive excitation codebook 102, a frame length n of an adaptive
excitation vector having the pitch period designated by pitch period
designation section 101, and outputs the result to evaluation measure
calculating section 107 as an adaptive excitation vector for pitch period
search (hereinafter abbreviated to "search adaptive excitation vector").

[0023]Synthesis filter 104 forms synthesis filters using the linear
prediction coefficients that are received as input on a per subframe
basis, generates impulse response matrixes of the synthesis filters based
on the subframe indices that are received as input on a per subframe
basis, and outputs the result to search impulse response matrix
generating section 105.

[0024]Using the impulse response matrix per subframe received as input
from synthesis filter 104, search impulse response matrix generating
section 105 generates an impulse response matrix per frame, based on the
subframe indices that are received as input on a per subframe basis, and
outputs the result to evaluation measure calculating section 107 as a
search impulse response matrix.

[0025]Search target vector generating section 106 generates a target
vector per frame using the target vectors that are received as input on a
per subframe basis, and outputs the result to evaluation measure
calculating section 107 as a search target vector.

[0026]Using the search adaptive excitation vector received as input from
search adaptive excitation vector generating section 103, the search
impulse response matrix received as input from search impulse response
matrix generating section 105 and the search target vector received as
input from search target vector generating section 106, evaluation
measure calculating section 107 calculates the evaluation measure for
pitch period search based on the subframe indices that are received as
input on a per subframe basis, and outputs the result to evaluation
measure comparison section 108.

[0027]Evaluation measure comparison section 108 calculates the pitch
period where the evaluation measure received as input from evaluation
measure calculating section 107 is the maximum, outputs an index IDX
indicating the calculated pitch period to the outside, and feeds back the
index IDX to adaptive excitation codebook 102.

[0029]If a subframe index that is received as input on a per subframe
basis indicates the first subframe, pitch period designation section 101
sequentially designates the pitch period T_int in a predetermined pitch
period search range, to search adaptive excitation vector generating
section 103. Here, the pitch period candidates in the pitch period search
range are determined by the total amount of information involved in
adaptive excitation vector quantization per subframe. For example, if the
amount of information involved in adaptive excitation vector quantization
is 4 bits for each of two subframes, the total amount of bits is 8 (=4+4)
bits, and therefore there are 256 patterns of pitch period candidates
from "32" to "287" in the pitch period search range. Here, "32" to "287"
indicate the indices indicating pitch periods. If a subframe index that
is received as input on a per subframe basis indicates the first
subframe, pitch period designation section 101 sequentially designates
the pitch period T_int (T_int=32, 33, . . . , 287) to search adaptive
excitation vector generating section 103, and, if a subframe index
indicates the second subframe, pitch period designation section 101 does
not designate pitch periods to search adaptive excitation vector
generating section 103.

[0030]Adaptive excitation codebook 102 has a built-in buffer storing
excitations, and, using an adaptive excitation vector having the pitch
period indicated by the index IDX fed back from evaluation measure
comparison section 108, updates the excitations every time the pitch
period search per frame is finished.

[0031]Search adaptive excitation vector generating section 103 cuts out,
from adaptive excitation codebook 102, a frame length n of the adaptive
excitation vector having the pitch period T_int designated by pitch
period designation section 101 and outputs the result to evaluation
measure calculating section 107 as the search adaptive excitation vector
P(T_int). For example, in a case where adaptive excitation codebook 102
is comprised of e vectors represented by exc(0), exc(1), exc(e-1), the
adaptive excitation vector P(T_int) generated in search adaptive
excitation vector generating section 103 can be represented by following
equation 1.

[0033]In FIG. 2, e represents the length of excitation 121, n represents
the length of the search adaptive excitation vector P(T_int), and
T13 int represents the pitch period designated by pitch period
designation section 101. As shown in FIG. 2, using the point that is
T_int apart from the tail end (i.e. position e) of excitation 121 (i.e.

[0034]adaptive excitation codebook 102) as the start point, search
adaptive excitation vector generating section 103 cuts out part 122 of a
frame length n in the direction of the tail end e from the start point,
and generates search adaptive excitation vector P(T_int). Here, if the
value of T_int is lower than n, search adaptive excitation vector
generating section 103 may duplicate the cut-out period until its length
reaches the frame length. Further, search adaptive excitation vector
generating section 103 repeats the cutting processing shown in the above
equation 1, for 256 patterns of T_int from "32" to "287" designated by
pitch period designation section 101.

[0035]Synthesis filter 104 forms a synthesis filter using input linear
prediction coefficients that are received as input on a per subframe
basis. Further, synthesis filter 104 generates the impulse response
matrix represented by following equation 2 if a subframe index that is
received as input on a per subframe basis indicates the first subframe,
while generating the impulse response matrix represented by following
equation 3 and outputting it to search impulse response matrix generating
section 105 if a subframe index indicates the second subframe.

[0036]As shown in equation 2, when the subframe index indicates the first
subframe, the impulse response matrix H of a frame length n is
calculated. Further, as shown in equation 3, when the subframe index
indicates the second subframe, the impulse response matrix H_ahead of a
subframe length m is calculated.

[0037]Taking into account that synthesis filter 104 varies between the
first subframe and the second subframe, search impulse response matrix
generating section 105 generates the search impulse response matrix H_new
represented by following equation 4 by cutting out components of the
impulse response matrixes H and H_ahead received as input from synthesis
filter 104, and outputs it to evaluation measure calculating section 107.

[0038]If a subframe index that is received as input on a per subframe
basis indicates the first subframe, search target vector generating
section 106 stores the target vector represented by X1=[x(0) x(2) . . .
x(m-1)] received as input. Further, if a subframe index that is received
as input on a per subframe basis indicates the second subframe, search
target vector generating section 106 generates the search target vector
shown in following equation 5 by adding the target vector represented by
input X2=[x(m) x(m+1) . . . x(n-1)] and the stored target vector X1, and
outputs the generated search target vector to evaluation measure
calculating section 107.

[5]

X=[x(0) x(1) . . . x(m-1) x(m) . . . x(n-1)] (Equation 5)

[0039]Using the adaptive excitation vector P(T_int) received as input from
search adaptive excitation vector generating section 103, the search
impulse response matrix H_new received as input from search impulse
response matrix generating section 105 and the target vector X received
as input from search target vector generating section 106, evaluation
measure calculating section 107 calculates the evaluation measure
Dist(T_int) for pitch period search according to following equation 6,
and outputs the result to evaluation measure comparison section 108. As
shown in following equation 6, evaluation measure calculating section 107
calculates, as an evaluation measure, the square error between the search
target vector generated in search target vector generating section 106
and the reproduced vector, which is acquired by convoluting the search
impulse response matrix H_new generated in search impulse response matrix
generating section 105 and the search adaptive excitation vector P(T_int)
generated in search adaptive excitation vector generating section 103.
Further, upon calculating the evaluation measure Dist(T_int) in
evaluation measure calculating section 107, instead of the search impulse
response matrix H_new in following equation 6, the matrix H'_new is
generally used which is acquired by multiplying the search impulse
response matrix H_new and the impulse response matrix W in the perceptual
weighting filter included in the CELP speech encoding apparatus (i.e.
H_new×W). However, in the following explanation, H_new and H'_new
are not distinguished, and both will be referred to as "H_new."

[0041]The CELP speech encoding apparatus including adaptive excitation
vector quantization apparatus 100 transmits speech encoded information
including the pitch period index IDX generated in evaluation measure
comparison section 108, to the CELP decoding apparatus including the
adaptive excitation vector dequantization apparatus according to the
present embodiment. The CELP decoding apparatus acquires the pitch period
index IDX by decoding the received speech encoded information and then
inputs the pitch period index IDX in the adaptive excitation vector
dequantization apparatus according to the present embodiment. Further,
like the speech encoding processing in the CELP speech encoding
apparatus, speech decoding processing in the CELP decoding apparatus is
also performed in subframe units, and the CELP decoding apparatus inputs
subframe indices in the adaptive excitation vector dequantization
apparatus according to the present embodiment.

[0042]FIG. 3 is a block diagram showing main components of adaptive
excitation vector dequantization apparatus 200 according to the present
embodiment.

[0044]If a subframe index indicates the first subframe, pitch period
deciding section 201 outputs the pitch period T_int' associated with the
pitch period index IDX received as input, to pitch period storage section
202, adaptive excitation codebook 203 and adaptive excitation vector
generating section 204. If a subframe index indicates the second
subframe, pitch period deciding section 201 reads the pitch period T_int'
stored in pitch period storage section 202 and outputs it to adaptive
excitation codebook 203 and adaptive excitation vector generating section
204.

[0045]Pitch period storage section 202 stores the pitch period T_int' of
the first subframe, which is received as input from pitch period deciding
section 201, and pitch period deciding section 201 reads the pitch period
T_int' in processing of the second subframe.

[0046]Adaptive excitation codebook 203 has a built-in buffer storing the
same excitations as the excitations provided in adaptive excitation
codebook 102 of adaptive excitation vector quantization apparatus 100,
and updates the excitations using the adaptive excitation vector having
the pitch period T_int' received as input from pitch period deciding
section 201 every time adaptive excitation decoding processing is
finished on a per subframe basis.

[0047]Adaptive excitation vector generating section 204 cuts out, from
adaptive excitation codebook 203, a subframe length m of the adaptive
excitation vector P'(T_int') having the pitch period T_int' received as
input from pitch period deciding section 201, and outputs the result as
the adaptive excitation vector per subframe. The adaptive excitation
vector P'(T_int') generated in adaptive excitation vector generating
section 204 is represented by following equation 7.

[0048]Thus, according to the present embodiment, in the CELP speech
encoding for performing linear prediction encoding in subframe units, the
adaptive excitation vector quantization apparatus forms a target vector,
an adaptive excitation vector and an impulse response matrix in frame
units using the linear prediction coefficient and linear prediction
residual vector in subframe units, and performs adaptive excitation
vector quantization on a per frame basis. By this means, it is possible
to suppress an increase of the amount of calculations, expand a range of
pitch period search and improve the accuracy of adaptive excitation
vector quantization and, furthermore, quality of CELP speech coding.

[0049]Further, although an example case has been described above with the
present embodiment where search impulse response matrix generating
section 105 calculates the search impulse response matrix represented by
above-described equation 4, the present invention is not limited to this,
and it is equally possible to calculate the search impulse response
matrix represented by following equation 8. Furthermore, without using
above-described equations 6 and 8, it is equally possible to calculate an
accurate search impulse response matrix according to the transition of
the synthesis filter between the first subframe and the second subframe.
However, in a case where an accurate search impulse response matrix is
calculated, the amount of calculations increases.

[0050]Further, although an example case has been described above with the
present embodiment where evaluation measure calculating section 107
calculates the evaluation measure Dist(T_int) according to
above-described equation 6 using the search target vector X of the frame
length n, the search adaptive excitation vector P(T_int) and the search
impulse response matrix H_new of the n×n matrix, the present
invention is not limited to this. Further, in evaluation measure
calculating section 107, it is equally possible to set in advance
constant r, where m≦r<n, newly form the search target vector X
of the length of constant r, the search adaptive excitation vector
P(T_int) of the length of constant r and the search impulse response
matrix H_new, which is a r×r matrix of the length of constant r, by
extracting elements up to the r-th order of search target vector X,
elements up to the r-th order of search adaptive excitation vector
P(T_int) and elements up to the r×r search impulse response matrix
H_new, and then calculate the evaluation measure Dist(T_int).

[0051]Further, although an example case has been described above with the
present embodiment where a linear prediction residual vector is received
as input and a pitch period of the linear prediction residual vector is
searched for with an adaptive excitation codebook, the present invention
is not limited to this, and it is equally possible to receive as input a
speech signal as is and directly search for the pitch period of the
speech signal.

[0052]Further, although an example case has been described above with the
present embodiment where 256 patterns of pitch period candidates from
"32" to "287" are used, the present invention is not limited to this, and
it is equally possible to set a different range for pitch period
candidates.

[0053]Further, although a case has been assumed and described with the
present embodiment where a CELP speech encoding apparatus including
adaptive excitation vector quantization apparatus 100 divides one frame
into two subframes and performs a linear prediction analysis of each
subframe, the present invention is not limited to this, and it is equally
possible to assume that a CELP speech encoding apparatus divides one
frame into three subframes or more and perform a linear prediction
analysis of each subframe. Further, in an assumption where each subframe
is further divided into two sub-subframes and a linear prediction
analysis of each sub-subframe is performed, it is equally possible to
apply the present invention. To be more specific, if a CELP speech
encoding apparatus calculates a linear prediction coefficient and linear
prediction residual by dividing one frame into two subframes, further
dividing each subframe into two sub-subframes and performing a linear
prediction analysis of each sub-subframe, adaptive excitation vector
quantization apparatus 100 needs to form two subframes with four
sub-subframes, form one frame with two subframes and perform a pitch
period search of the resulting frame.

[0054]The adaptive excitation vector quantization apparatus and adaptive
excitation vector dequantization apparatus according to the present
invention can be mounted on a communication terminal apparatus in a
mobile communication system that transmits speech, so that it is possible
to provide a communication terminal apparatus having the same operational
effect as above.

[0055]Although a case has been described above with the above embodiments
as an example where the present invention is implemented with hardware,
the present invention can be implemented with software. For example, by
describing the adaptive excitation vector quantization method and
adaptive excitation vector dequantization method according to the present
invention in a programming language, storing this program in a memory and
making the information processing section execute this program, it is
possible to implement the same function as the adaptive excitation vector
quantization apparatus and adaptive excitation vector dequantization
apparatus according to the present invention.

[0056]Furthermore, each function block employed in the description of each
of the aforementioned embodiments may typically be implemented as an LSI
constituted by an integrated circuit. These may be individual chips or
partially or totally contained on a single chip.

[0057]"LSI" is adopted here but this may also be referred to as "IC,"
"system LSI," "super LSI," or "ultra LSI" depending on differing extents
of integration.

[0058]Further, the method of circuit integration is not limited to LSI's,
and implementation using dedicated circuitry or general purpose
processors is also possible. After LSI manufacture, utilization of an
FPGA (Field Programmable Gate Array) or a reconfigurable processor where
connections and settings of circuit cells in an LSI can be reconfigured
is also possible.

[0059]Further, if integrated circuit technology comes out to replace LSI's
as a result of the advancement of semiconductor technology or a
derivative other technology, it is naturally also possible to carry out
function block integration using this technology. Application of
biotechnology is also possible.

[0060]The disclosure of Japanese Patent Application No. 2006-338342, filed
on Dec. 15, 2006, including the specification, drawings and abstract, is
included herein by reference in its entirety.